Abstract
Objective:
To examine the effect of a telehealth intervention that used a dietary app, educational website, and weekly dietitian tele-counseling) on sodium intake, diet quality, blood pressure and albuminuria among individuals with diabetes and early stage chronic kidney disease.
Design and Methods:
We examined the effects of a dietary app-supported tele-counseling intervention in a single center, single arm study of 44 participants with type 2 diabetes and stage 1–3a chronic kidney disease. Participants recorded and shared dietary data via MyFitnessPal with registered dietitians, who used motivational interviewing to provide telephone counseling weekly for 8 weeks. After the 8-week intensive intervention, participants were followed at 6 months and 12 months. Outcomes included 24-hour urine sodium (2 collections per timepoint), Healthy Eating Index (HEI) 2015 score (three 24-hour dietary recalls per timepoint), 24-hour systolic blood pressure (SBP) and diastolic blood pressure (DBP), and 24-hour urine albumin excretion.
Results:
Out of 44 consented participants (mean age 60.3 +/− 11.9 years, 43% female, 89% white, median eGFR was 78.5 ml/min/1.73m2, median urine albumin excretion 52.9 mg/day, 84% hypertension), 32 (73%) completed 8-week follow-up, 27 (61%) completed 6-month follow up, and 25 (57%) completed 12-month follow up. Among participants who completed 12-month follow up, reported sodium intake decreased by 638 mg/day from baseline of 2919 mg/day (p-value <0.001). The 24-hour mean urine sodium and albumin excretion did not decline over the study period. HEI-2015 score improved by 7.76 points at 12 months from a mean baseline of 54.6 (p-value <0.001). Both 24-hour SBP and DBP declined at 12 months from baseline (SBP −5.7 [95% CI: −10.5, −1.0] mmHg, p-value=0.02 and DBP −4.1 [95% CI: −7.2, −1.1] mmHg, p-value=0.01)
Conclusion:
Overall, this study demonstrates that a short, intensive, remotely delivered dietary intervention for adults with type 2 diabetes and early chronic kidney disease at high risk for disease progression and cardiovascular complications led to improvement in blood pressure and self-reported sodium intake and diet quality, but no improvement in albuminuria. Future research studies are needed to examine whether remotely delivered dietary interventions can ultimately improve kidney health over time.
Keywords: telehealth, diabetes, nutrition counseling, chronic kidney disease, chronic renal failure
Background:
Healthy dietary patterns have been associated with decreased risk of incident chronic kidney disease (CKD) and decreased risk of death in patients with diabetes or with CKD (1, 2). Factors such as high sodium intake, high animal fat intake, low intake of fruits and vegetables have been associated with worse outcomes (3–5). Randomized controlled trials (RCTs) suggest that increasing intake of fruits and vegetable intake lowers dietary acid load and may reduce the risk of kidney function decline (6), and consumption of a Mediterranean-style diet can reduce the incidence of major cardiovascular events (7). In the Action for Health in Diabetes (Look AHEAD) trial, intensive lifestyle intervention in adults with type 2 diabetes and overweight/obesity reduced risk of CKD by ~30% (8).
One important strategy to reduce CKD progression is nutrition counseling. A RCT in South Korean patients with hypertension and albuminuria found that an intervention using weekly telephone calls to deliver low-salt diet education was significantly associated with lower salt intake and reducing albuminuria (9). Among US adults with diabetes, only 5% achieved dietary targets recommended by the American Diabetes Association (ADA) (<10% total calories from saturated fat, <2300 mm sodium/day, ≥ 14 g fiber/1000 kcal, <300 mg cholesterol/day)(10), yet as few as 10% of patients with diabetes or CKD receive ADA-recommended medical nutrition therapy (MNT) from a Registered Dietitian/Nutritionist (dietitian) (11–14).
Due to the growing popularity of smartphones and expanded use of telehealth necessitated by the coronavirus disease-2019 (COVID-19) pandemic (15), an opportunity exists for using dietary applications (apps) to improve dietary quality and health outcomes. However, evidence for app-based behavioral health interventions remains weak (16). This study aimed to examine the effect of a telehealth intervention (dietary app, educational website, weekly dietitian tele-counseling) on sodium intake, diet quality, blood pressure and albuminuria. We hypothesized that 24-hour urine sodium excretion and blood pressure would decrease while reported diet quality would improve from baseline to 12 months after participation in the telehealth intervention.
Methods:
Study Design
This was a single center, single arm study approved by the Institutional IRB. Participants were recruited and consented from February 13, 2017 to November 30, 2017. The study was registered on clinicaltrials.gov (NCT03015480).
Study Population
This study recruited patients receiving care from one academic medical center (Geisinger) with diabetes and stage 1–3a CKD as determined by electronic health record data. Inclusion criteria included: age 21 years or older, diabetes mellitus, last urine albumin/creatinine ratio ≥ 30 mg/g measured in the last 2 years, at least 2 outpatient eGFR measurements in electronic health record (EHR) separated by at least 2 years with the last outpatient eGFR ≥45 ml/min/1.73m2 within the past year and eGFR decline rate at least −2 ml/min/1.73m2/year, last outpatient SBP <160 mmHg and DBP <100 mmHg in past year, agreeable to change diet, and having access to a smartphone or the internet. Exclusion criteria included inability to understand English, self-reported average consumption of >14 alcohol beverages/week, psychiatric hospitalization in past year, urine ACR >2500 mg/g, last potassium >5.0 mg/dL, unstable angina, recent cardiovascular or hypoglycemic event, malignancy, and planned bariatric surgery.
Intervention
The intervention was informed by the situated learning and control theories (17, 18), discussions with the Geisinger’s Kidney Patient Advisory Council and Patient Advisory Council for Obesity, and a prior 8-week pre-post, mixed methods feasibility study of 16 patients with stage 1–3a CKD (19). The intervention structure was an 8-week intensive phase consisting of weekly tele-counseling sessions with a dietitian for 15–30 minutes, daily emails with nutrition tips, weekly summary emails of nutrition intake with tailored messages, and access to a study-developed patient education website. During the maintenance period, tele-counseling with a dietitian occurred at 5 and 11 months post-baseline and a nutritionist-led tour was offered at a local grocery chain (Weis Markets, Inc.). During the intensive phase, participants were instructed to use MyFitnessPal (www.myfitnesspal.com) daily, a web-based app for self-monitoring diet. Participants’ accounts were linked to the study team to enable sharing of their dietary data and thus dietitians were able to review participants’ sodium intake, fruit/vegetable intake, and red and/or processed meat intake to inform tailored tele-counseling. Dietitians used motivational interviewing techniques for the tele-counseling sessions. Dietary goals focused on reducing sodium intake, increasing fruit and vegetable intake, and minimizing intake of red meat and/or processed foods. Given uncertainties of the impact of low-protein diet in patients with diabetic kidney disease (20), we did not specifically recommend a low-protein diet but rather recommended replacing protein from red and/or processed meat with plant-based or lean meat sources. Participants received daily emails with nutritional tips and weekly summaries of their sodium, fruit/vegetable intake with individualized messages to reinforce personalized goals. Participants were instructed to access a study-developed educational website that featured weekly topics including: self-monitoring diet, strategies and goal-setting to eat a more healthful diet, healthful eating on the go, healthful eating with family and friends, relapse prevention, time and money management, and stimulus control.
Recruitment
Patients within the EHR who met the inclusion criteria were identified and sent a notification letter including study information. The letter was then followed up with a call to assess interest in the study and pre-screen for eligibility. Those who passed the pre-screen were scheduled for the eligibility study visit. Experience from previous studies involving early CKD patients has demonstrated that recruitment using EHR is essential as many are unaware of their stage 1–2 CKD diagnosis. As such, a partial Health Insurance Portability and Accountability Act (HIPAA) waiver was granted as the study would be impractical otherwise.
Procedures/Data Collection
In total, there were five in-person research visits (two at baseline and three during the maintenance phase) (Figure 1). After providing informed consent at the first visit, participants had weight and waist circumference objectively measured, completed medical history, an eHealth literacy survey (21) and generalized self-efficacy survey (22). In addition, they had a 24-hour ambulatory blood pressure monitoring (ABPM) device placed (Spacelabs Ontrak, Snoqualmie, Washington) (23) and were instructed to collect two 24-hour urine collections on consecutive days, and complete 3 telephone-administered 24-hour dietary recalls (goal of 2 weekdays, 1 weekend day) with trained staff from the Johns Hopkins Institute for Clinical and Translational Research (ICTR) Nutrition Center. The 24-hour dietary recalls followed the USDA multi-pass method.(24) The second visit occurred the following week. At this visit, participants also completed questionnaires that assessed technology use, food habits, health literacy (25), dietary knowledge (26, 27), dietary sodium knowledge (28), and stage of change for reducing sodium intake, increasing fruit/non-starchy vegetable intake, and decreasing red and/or processed meat intake (29). A study team member assisted participants with setting up an account on MyFitnessPal (download app onto smartphone or use a computer to access website), instructed on its use, and provided a brochure that explained the educational website.
Figure 1.
Study Visits: Procedures and Data collection.
After completion of the 8-week intensive intervention phase, participants completed the third in-person visit, where they had weight measured and self-administered study questionnaires (eHealth literacy, health literacy, dietary knowledge, sodium knowledge, self-efficacy, stage of change). They had a 24-hour ABPM placed, were asked to perform two 24-hour urine collections on consecutive days, and complete a set of three 24-hour dietary recalls with ICTR staff by telephone. At 6 months follow up, participants were asked to complete a set of three 24-hour dietary recalls with ICTR staff, perform two 24-hour urine collections on consecutive days, undergo 24-hour ABPM, and complete self-administered study questionnaires (eHealth literacy, health literacy, sodium knowledge, self-efficacy). At 12 months follow up, participants were asked to complete a set of three 24-hour dietary recalls with ICTR staff, perform two 24-hour urine collections on consecutive days, undergo 24-hour ABPM, and complete self-administered study questionnaires (eHealth literacy, health literacy, sodium knowledge, self-efficacy). Participants were compensated for each set of the 24-hour dietary recalls, urine collections and ABPM that were completed: $25 gift cards for week 1, $25 for week 8, $100 for 6-month, and $100 for 12-month. In addition, participants received a $50 gift card upon completion of the 8-week intervention period if they entered at least 3 meals per day for at least 80% of the intervention period. After completion of the 12-month visit, participants were mailed personalized reports that summarized their 24-hour blood pressure and 24-hour urine sodium (Supplemental Item 1), and dietary data (Supplemental Item 2). No medication changes were made by the study team or specifically recommended during the research study though 24-hr ABPM reports were sent electronically to patients’ primary care providers.
Study Outcome Measures
The primary outcome was change in mean 24-hour urine sodium at 12 months compared to baseline. Mean 24-hour urine sodium was calculated from two 24-hour urine sodium collections, which were corrected for total time of collection. Secondary outcomes included clinical, urine, and self-reported dietary measures, and study adherence. Clinical measures included: changes from baseline to 12 months in 24-hour ambulatory systolic blood pressure, and weight. Urine measures included: urine sodium/potassium ratio, 24-hour urine albumin excretion, 24-hour urine phosphorus, and net endogenous acid production (calculated as −10.2 + 54.5 (protein [g/d]/potassium [mEq/d] (baseline to 12 months), unadjusted and adjusted for creatinine excretion, Dietary measures included: Healthy Eating Index (HEI)-2015 score (a measure of diet quality that uses a scoring system (0 to 100) to evaluate 13 components of foods that align with dietary recommendations, with ideal overall score of 100 (30)) that was based on dietary intake collected from 24-hour recalls; protein intake (grams/day) was estimated using 24-hour dietary data as well as from urine collections per the Maroni equation (Urinary urea nitrogen + (weight [kg] × 0.031)) * 6.25 g)(31). Study feasibility was measured by adherence, which was assessed by examining the frequency of dietary data entry determined by study dietitians who tracked the number of days per week the participant completed at least 3 eating occasions/day and the number of completed weekly dietitian phone calls during the intervention.
Analytic Considerations
In a previous lifestyle intervention study, the standard deviation of change in 24-hour urine sodium was 1300 mg/d (32). Assuming the baseline sodium intake was ~3600 mg/d, we estimated that we would have >80% power to detect a 700 mg/d difference in 24-hour urine sodium excretion between baseline and 12-months, with a sample size of 30 individuals completing 12-month follow-up, at an alpha level of 0.05. We used generalized estimating equations, clustered by individuals, to examine changes in the outcomes over time. Sensitivity analyses were conducted excluding patients with potentially unreliable 24-hour urine collections (>30% coefficient of variation for 24-hour urine creatinine). Analyses were performed using STATA/MP 15.1 (College Station, TX).
Results
Out of 913 patients who were contacted by telephone, 44 participants consented and completed baseline testing and were included in the statistical analyses (Table 1). The mean age of participants was 60.3 years, 43.2% were female, 89% were white, median eGFR was 78.5 (interquartile interval [IQI] 58.3, 96) ml/min/1.73m2, and median 24-hour urine albumin excretion was 52.9 (30.9, 142.0) mg/day. Almost half (45.5%) of the participants were retired, and 20.5% had a household income of less than $25,000. Most participants reported they had baseline hypertension (84.1%) and/or dyslipidemia (81.8%). Mean BMI of participants was 31.8 kg/m2.
Table 1.
Study participant baseline characteristics
| Characteristic | Total (N=44) |
Completers (N=23) |
Non-Completers (N=21) |
p-value |
|---|---|---|---|---|
| Age, mean (SD), years | 60.3 (11.9) | 67.0 (9.0) | 53.0 (10.4) | <0.001 |
| Female | 19 (43.2%) | 9 (35%) | 11 (52%) | 0.2 |
| Non-Hispanic White | 39 (88.6%) | 23 (96%) | 17 (81%) | 0.2 |
| Hispanic | 3 (6.8%) | 1 (4%) | 2 (10%) | |
| African American | 2 (4.5%) | 0 (0%) | 2 (10%) | |
| Education level | 0.4 | |||
| High school | 17 (38.6) | 9 (39%) | 8 (38%) | |
| Some college or trade school | 12 (27.3) | 4 (17%) | 8 (38%) | |
| College degree | 12 (27.3) | 8 (35%) | 4 (19%) | |
| Graduate or Professional school | 3 (6.8) | 2 (9%) | 1 (5%) | |
| Annual Household Income | 0.5 | |||
| <$25,000 | 9 (21%) | 3 (13%) | 6 (29%) | |
| $25–50,000 | 14 (32%) | 7 (30%) | 7 (33%) | |
| $50–75,000 | 7 (16%) | 3 (13%) | 4 (19%) | |
| $75,000–100,000 | 9 (21%) | 6 (26%) | 3 (14%) | |
| >=$100,000 | 3 (7%) | 2 (9%) | 1 (5%) | |
| Not reported | 2 (5%) | 2 (9%) | 0 (0%) | |
| Employment status | 0.01 | |||
| Full-time | 10 (22.7%) | 4 (17%) | 6 (29%) | |
| Part-time | 6 (13.6%) | 2 (9%) | 4 (19%) | |
| Unemployed | 1 (2.3%) | 0 (0%) | 1 (5%) | |
| Retired | 20 (45.5%) | 16 (70%) | 4 (19%) | |
| Disabled | 7 (15.9%) | 1 (4%) | 6 (29%) | |
| Smoking status | 0.5 | |||
| Current | 1 (2.3%) | 0 (0%) | 1 (5%) | |
| Former | 21 (47.7%) | 12 (52%) | 9 (43%) | |
| Never | 22 (50.0%) | 11 (48%) | 11 (52%) | |
| Hypertension | 37 (84.1%) | 21 (91%) | 16 (76%) | 0.2 |
| Dyslipidemia | 36 (81.8%) | 19 (83%) | 17 (81%) | 0.9 |
| Body Mass Index, mean (SD), kg/m2 | 31.8 (10.9) | 29.6 (11.5) | 34.1 (10.0) | 0.2 |
| 24-h Systolic Blood Pressure, mean (SD), mmHg | 129.7 (12.9) | 128.4 (11.8) | 131.0 (14.1) | 0.5 |
| 24-h Diastolic Blood Pressure, mean (SD), mmHg | 73.1 (10.4) | 71.2 (10.9) | 75.2 (9.7) | 0.2 |
| eGFR, median (IQR), ml/min/1.73m2 | 78.5 (58.3, 96) | 67.0 (54.0, 86.0) | 91.0 (73, 98) | 0.01 |
| Albuminuria, median (IQR), mg/d | 52.9 (30.9, 142.0) | 69.4 (28.9, 162.3) | 48.3 (32.7, 114.0) | 0.3 |
| CKD stage | 0.04 | |||
| 1 (90+) | 15 (34.1%) | 4 (17%) | 11 (52%) | |
| 2 (60–89) | 17 (38.6%) | 10 (43%) | 7 (33%) | |
| 3a (45–59) | 12 (27.3%) | 9 (39%) | 3 (14%) | |
| Hemoglobin A1c | 7.7% (1.6%) | 7.8% (1.7%) | 7.6% (1.6%) | 0.7 |
| Obstructive Sleep Apnea | 13 (30.2%) | 6 (26%) | 7 (33%) | 0.6 |
| Neuropathy | 24 (54.6%) | 13 (57%) | 11 (52%) | 0.8 |
| Retinopathy | 13 (29.6%) | 7 (30%) | 6 (29%) | 0.9 |
| Mood disorder | 15 (34.1%) | 7 (30%) | 8 (38%) | 0.6 |
| Atrial fibrillation | 14 (31.8%) | 8 (35%) | 6 (29%) | 0.7 |
| ASCVD | 15 (34.1%) | 10 (43%) | 5 (24%) | 0.2 |
| Cirrhosis | 3 (6.8%) | 1 (4%) | 2 (10.0%) | 0.5 |
| HFrEF | 4 (9.1%) | 2 (9%) | 2 (10.0%) | 0.9 |
SD – standard deviation
IQR- interquartile range
eGFR – estimated glomerular filtration rate
ASCVD – Atherosclerotic Cardiovascular Disease
HFrEF – Heart Failure with reduced Ejection Fraction
Adherence to intervention
Of the 44 study participants who completed baseline 24-hour urine collection, 5 withdrew within the first week, only 32 completed the 8-week follow-up visit and 24-hour urine collection, 27 completed 26-week follow-up visit 24-hour urine collection, and 23 completed the 52-week follow-up 24-hour urine collection (Figure 2). Most participants did not provide a reason for withdrawing from the study. Among those provided a reason these included: not wanting to “do the work any longer”, learning completed, residence in an assisted living facility with little control over their diet, and new malignancy diagnosis. Among the remaining enrolled participants, weekly adherence to logging dietary intake at least 5 days per week ranged from 58–66% and adherence to weekly calls with dietitians ranged between 71–84% (Supplemental Table 1). Participants who completed the study were older, had lower eGFR, and tended to be more educated, male, retired, and have a history of atherosclerotic cardiovascular disease (Table 1).
Figure 2.
Participant Flow Diagram
Changes in Sodium Intake and Excretion
The reported mean sodium intake at 12 months decreased by 638 mg/day from a baseline mean of 2919 mg/day (p-value <0.001). The 24-hour mean urine sodium did not decline over the study period (Figure 3, Table 2). In sensitivity analyses excluding participants with potentially unreliable 24-hour urine collections (>30% coefficient of variation) results were similar (data not shown).
Figure 3.
Changes in Sodium, Potassium, and Protein Intake by dietary recall and 24-hour urine collections.
Table 2.
Changes in Sodium, Potassium, and Protein Intake, and Healthy Eating Index-2015 Scores
| Baseline* | Δ Baseline-8 weeks† | P value | Δ Baseline-6 months† | P value | Δ Baseline-12 months† | P value | |
|---|---|---|---|---|---|---|---|
| Data from mean of two 24-hour urine collections at each timepoint | |||||||
| Urine sodium, mg/day | 3372 (1399) | −232 (−742, 278) | 0.4 | 169 (−351, 689) | 0.5 | 487 (−135, 1109) | 0.1 |
| Urine potassium, mg/day | 2380 (866) | 344 (−45, 732) | 0.1 | 346 (0, 692) | 0.05 | 495 (125, 866) | 0.01 |
| Urine sodium/potassium ratio, mmol/mmol | 4.33 (1.66) | −0.72 (−1.26, −0.19) | 0.008 | 0.02 (−0.80, 0.84) | 1.0 | −0.31 (−1.00, 0.39) | 0.4 |
| Urine phosphorus, mg/day | 651 (307) | 0.11 (−0.01, 0.22) | 0.07 | 0.09 (−0.02, 0.20) | 0.1 | 0.16 (0.02, 0.30) | 0.03 |
| Net excretion acid production, mEq/day | 60.0 (26.8) | −2.24 (−10.57, 6.09) | 0.6 | 6.20 (−5.31, 17.71) | 0.3 | −2.57 (−12.28, 7.14) | 0.6 |
| Protein intake estimated from urine‡ (g/day) | 72.4 (25.6) | 10.0 (−0.6, 20.6) | 0.06 | 10.8 (3.7, 17.9) | 0.003 | 13.1 (3.9, 22.2) | 0.005 |
| Data from mean of three 24-hour dietary recalls at each timepoint | |||||||
| Sodium intake, mg/d | 2919 (1140) | −506 (−789, −224) | <0.001 | −361 (−685, −37) | 0.03 | −638 (−969, −308) | <0.001 |
| Potassium intake, mg/d | 2486 (770) | 220 (−54, 494) | 0.1 | 234 (−23, 490) | 0.07 | 94 (−152, 341) | 0.5 |
| Diet sodium/potassium ratio, mmol/mmol | 2.07 (0.83) | −0.53 (−0.81, −0.24) | <0.001 | −0.45 (−0.70, −0.21) | <0.001 | −0.50 (−0.76, −0.24) | <0.001 |
| Protein intake (g/day) | 83.4 (29.3) | −2.96 (−12.02, 6.10) | 0.5 | −6.15 (−13.66, 1.37) | 0.1 | −6.82 (−16.18, 2.53) | 0.2 |
| HEI-2015 total score § | 54.6 (12.9) | 7.72 (2.93, 12.50) | 0.002 | 7.41 (2.73, 12.08) | 0.002 | 7.76 (3.57, 11.95) | <0.001 |
| HEI-2015 component scores | |||||||
| Total fruits | 2.18 (1.58) | 1.10 (0.50, 1.69) | <0.001 | 0.88 (0.17, 1.58) | 0.02 | 0.77 (0.25, 1.29) | 0.004 |
| Whole fruit | 2.71 (1.74) | 0.95 (0.22, 1.68) | 0.01 | 0.97 (0.25, 1.68) | 0.01 | 0.92 (0.29, 1.54) | 0.004 |
| Total vegetables | 2.98 (1.38) | 0.88 (0.38, 1.38) | 0.001 | 0.69 (0.20, 1.18) | 0.01 | 0.45 (−0.14, 1.04) | 0.1 |
| Greens and beans | 1.60 (1.77) | −0.03 (−0.62, 0.55) | 0.9 | 0.35 (−0.42, 1.13) | 0.4 | −0.24 (−0.95, 0.47) | 0.5 |
| Whole grains | 3.66 (3.25) | 0.61 (−0.66, 1.88) | 0.4 | 1.11 (−0.38, 2.59) | 0.1 | 1.04 (−0.38, 2.46) | 0.2 |
| Dairy | 5.57 (2.68) | −0.49 (−1.39, 0.41) | 0.3 | 0.46 (−0.45, 1.38) | 0.3 | −0.01 (−0.91, 0.88) | 1.0 |
| Total protein foods | 4.66 (0.47) | 0.02 (−0.18, 0.22) | 0.8 | −0.17 (−0.43, 0.10) | 0.2 | −0.05 (−0.24, 0.13) | 0.6 |
| Seafood and plant proteins | 1.87 (1.54) | 0.25 (−0.43, 0.92) | 0.5 | 0.21 (−0.50, 0.92) | 0.6 | 0.31 (−0.28, 0.89) | 0.3 |
| Fatty acids | 4.71 (2.83) | 0.74 (−0.36, 1.83) | 0.2 | 0.45 (−0.63, 1.52) | 0.4 | 0.60 (−0.47, 1.66) | 0.3 |
| Refined grains | 6.82 (2.38) | 1.10 (0.20, 2.00) | 0.02 | 0.74 (−0.17, 1.64) | 0.1 | 0.50 (−0.34, 1.34) | 0.2 |
| Sodium | 4.19 (2.55) | 1.48 (0.48, 2.49) | 0.004 | 1.34 (0.36, 2.31) | 0.01 | 2.13 (1.33, 2.93) | <0.001 |
| Saturated Fat | 5.11 (2.86) | 0.91 (−0.24, 2.06) | 0.1 | 0.74 (−0.26, 1.75) | 0.1 | 1.22 (0.31, 2.12) | 0.01 |
| Added Sugars | 8.54 (1.76) | 0.47 (−0.12, 1.05) | 0.1 | −0.03 (−0.73, 0.67) | 0.9 | 0.31 (−0.39, 1.00) | 0.4 |
Baseline values are presented as mean (SD).
Means (95% confidence interval) of changes in each of the variables is shown.
estimated by Maroni equation
HEI-2015 – Healthy Eating Index-2015, score range 0–100
Changes in 24-hour Ambulatory Blood Pressure
Both 24-hour ambulatory systolic blood pressure (SBP) and diastolic blood pressure (DBP) declined at 12 months from baseline (SBP −5.7 [95% CI: −10.5, −1.0] mmHg, p-value=0.02 and DBP −4.1 [95% CI: −7.2, −1.1] mmHg, p-value=0.01) (Figure 4, Supplemental Table 2).
Figure 4.
Changes in systolic and diastolic lood pressure assessed by 24-hour ambulatory blood pressure monitoring.
Changes in Healthy Eating Index Score, Protein Intake, and Weight
The mean HEI-2015 score at baseline was 54.6 and improved by 7.76 at 12 months (p-value <0.001) (Table 2). Of the individual dietary components of the HEI-2015, total fruits, whole fruit, sodium and saturated fat scores improved by 12 months (total fruits by 0.77 [95% CI: 0.25–1.29], whole fruit by 0.92 [95% CI: 0.29–1.54], sodium by 2.13 [95% CI: 1.33–2.93], saturated fat by 1.22 [95% CI: 0.31–2.12], all p-values ≤0.01), with the total vegetable score improving only until 6 months (by 0.69 [95% CI: 0.20–1.18], p-value=0.01), and refined grains only improved at 8 weeks (by 1.10 [95% CI: 0.20–2.00], p-value=0.02). Protein intake (as estimated with the Maroni equation) increased at 12 months from baseline (by 13.1 [95% CI: 3.9–22.2) grams/day). By contrast, protein intake estimated by the 24-hour dietary recalls did not significantly change (Table 2, Figure 3). Weight decreased from baseline to 8 weeks (−3.99 lb, 95% CI: −6.04, −1.95; p<0.001) but was not measured at the 6-month and 12-month visits.
Changes in Potassium, Phosphorous and Albumin Excretion, and Net Acid Production
There was an increase in urine potassium excretion by 495 mg/day at 12-months from baseline (p-value=0.01) (Figure 3, Table 2). Urine phosphorous excretion increased at 12 months (by 0.16 (95% CI: 0.02, 0.30) mg/day). Albuminuria and net acid production did not change throughout the study period (Supplemental Table 2).
Discussion
Among adults with type 2 diabetes and early chronic kidney disease, participation in a remotely delivered 8-week intervention that used mobile health technology and dietitian tele-counseling was associated with sustained improvement of self-reported sodium intake, overall diet quality, fruit consumption, and 24-hour blood pressure at 12 months. There were no changes detected in urine sodium or albuminuria after the intervention. Adherence was good with the self-monitored diet mHealth app and participation in dietitian phone calls during the 8-week intervention period (70–80%), but by the 12-month visit a considerable number of individuals had withdrawn. Overall, this study demonstrates that a short, intensive, remotely delivered dietary intervention is a feasible approach to help manage adults with diabetes and early CKD who are at high risk for CKD progression and cardiovascular complications.
Lowering sodium intake has a well-documented effect on blood pressure (33) and albuminuria (34, 35). In a randomized cross-over study of patients with CKD who underwent a high sodium (mean 168 mEq/day) and low sodium diet (mean 75 mEq/day), blood pressure decreased by 10/4 mmHg and albuminuria was nearly halved during the low sodium period (34). In our study, there was a sustained decrease in self-reported intake of sodium (−638 mg/day) and 24-hour ambulatory blood pressure (−5.7/−4.1 mmHg), but no reduction in 24-hour urine sodium or albuminuria. The improvement in blood pressure could be explained by observed increases in fruit and vegetable intake and increased urinary potassium excretion(36), although no change in potassium intake estimated from dietary recalls. The lack of agreement in change in potassium and protein intake (increased using urine data, no change using dietary data) suggests the possibility that dietary intake may have been underreported during follow-up visits by participants. The lack of improvement in albuminuria may be due to insufficient reduction in sodium intake or an observed increase in protein intake (calculated from 24-hour urine urea excretion) (37).
Although 24-hour urine sodium excretion has been considered to be the gold standard for assessing sodium intake, 24-hour urine collections are prone to errors, primarily due to sodium handling and variability of salt-sensitivity. For example, in a highly controlled study of cosmonauts under optimal research conditions, several 24-hour urine collections resulted in just 75% accuracy in classifying a 575 mg difference in sodium intake (38). This may also be the reason that two other recently published sodium reduction studies in CKD reported nonsignificant changes in 24-hour urinary sodium excretion by the end of six and nine-month follow-up periods (39, 40). Additionally, the variability in urinary sodium excretion could be explained by salt sensitivity and the ability to store salt in reservoirs located in the skin.(41) Further work is needed to better assess dietary sodium intake and determine optimal strategies to reduce sodium intake and albuminuria through lifestyle modification.
In addition to high sodium levels, Western-style diets are also typically higher in acid-producing foods (e.g., meat, cheeses) and lower in base-producing foods, such as fruits and vegetables. Observational studies have found that dietary acid load is associated with an increased risk of CKD progression(42). A study by Goraya et al. has shown that increased consumption of fruits and vegetables in patients with stage 3 CKD can reduce dietary acid load by 30%, decrease albuminuria by nearly 20%, slow the decline in eGFR(6), with costs comparable to oral sodium bicarbonate(43). Our study demonstrated that after an 8-week remotely delivered dietary intervention using self-monitoring with a health-related app and motivational interviewing delivered by dietitians over the phone, there was sustained improvement in fruit consumption and an early improvement in vegetable consumption, which could ultimately lead to a decrease in acid production and potentially mitigate CKD progression.
Similar to the lack of relationship with reduced sodium intake, we did not observe a decline in albuminuria with an increased consumption of base producing foods. This absence of an association could be attributed to a non-decrease in the net acid production, which we hypothesize is related to an increase in protein and phosphorous consumption that offset the benefits of the increased fruit and vegetable consumption. The increased protein consumption occurred for unclear reasons. Perhaps in the effort to decrease sodium intake, participants consumed more protein as the dietary intervention did not specifically recommend a low protein diet, and instead focused on reducing sodium and red and/or processed meat and increasing fruit and vegetable intake and plant-based or lean protein sources, given the uncertainties of the effectiveness of low protein diet in diabetes. Although reducing protein from animal sources and increasing protein from vegetable sources is thought to reduce acid production and metabolic acidosis, these effects are mostly observed with very low protein diets (e.g., 0.3 to 0.5 g/kg protein/kg per day), which are not currently recommended for individuals with diabetic kidney disease (44). Future research efforts should focus on establishing the optimal dietary pattern and recommendation of a particular protein type in diabetic kidney disease.
Recruitment and retention in our study was challenging and may be informative for future trials. Only 44 (4.8%) of 913 potential patients ended up meeting inclusion/exclusion criteria and completing baseline testing. The low participation rate <5% is similar to some other telehealth interventions.(45, 46) Future studies may need to consider incorporating personalized estimates of risks(47), better integration with primary care practices (48) and minimizing data collection to enhance recruitment. From a retention perspective, over a third of participants withdrew from the study. Based on reasons for withdrawal, data collection burden may have been a modifiable factor. Other intensive lifestyle intervention trials have optimized retention with a run-in period to ensure data collection is not burdensome to participants. Additionally, to our knowledge, study duration may have been a factor as our study followed participants longer than any of the previously published sodium reduction studies for adults with CKD. The longer the duration, the greater the opportunity for lags in participant engagement, particularly with remotely delivered interventions that suffer from a lack of sustained involvement (49, 50). Reasons for decreased engagement may reflect early newness of the mobile app or intervention, and as the novelty wears off, decreased interest and less engagement. In order for mHealth technologies to facilitate meaningful change, effective patient engagement strategies are needed. Future RCTs are needed to examine whether remotely delivered interventions can ultimately improve kidney health over time.
Strengths of this study include the utilization of novel approaches to teach and monitor dietary interventions in patients with early kidney disease, which has not been systematically evaluated in this population. We demonstrated use of remotely delivered nutritional intervention using a free dietary app and remote teaching methods via phone calls with dietitians. This approach has the potential to be used in large scale settings where access to dietary counseling is limited or expensive. Additionally, utilizing multi-modal methods of data collection, including multiple 24-hour dietary recalls, 24-hour urine collections and ambulatory blood pressure monitoring further strengthens the validity of the results. This study also focused on a high-risk chronic kidney disease population including elderly, low income individuals, and the findings guide management or interventions in such populations. There are several limitations to discuss. First, there was high drop-out rate with a difference in those who completed the study, compared to those who did not, which limits generalizability and introduces selection bias. We had powered the study based on retaining 30 participants to end of follow up but only had 27, limiting our power to evaluate study outcomes. Almost all of the participants who attended the 8-week follow-up visit completed the study, suggesting that a run-in period would be helpful with retention in future interventions. Data on usage of the study website were not collected. Additionally, the study population was mostly white and recruited from rural areas, further limiting the generalizability. A larger RCT with diverse racial and ethnic minority groups are necessary. Third, the primary endpoint of change in 24-hour urine sodium was likely too ambitious given that the remote delivery of the nutritional intervention has not yet be validated. Perhaps, focusing on a feasibility outcome, such as greater than 70% adherence at 6-month visit would have been more appropriate of a primary outcome. Fourth, while we did not specifically make or recommend blood pressure medication changes, we did not collect data on medication changes that could have occurred during the follow-up period. Lastly, there was no control group, so comparisons and the assessment of efficacy is limited.
Conclusion
Among adults with type 2 diabetes and early chronic kidney disease, participation in an 8-week intervention that used mobile health technology and dietitian tele-counseling was associated with sustained improvement in reported sodium intake and diet quality, as well as measured blood pressure. There were no changes detected in urine sodium or albuminuria. While adherence to dietary data and dietitian phone calls was satisfactory, one-quarter of participants withdrew or were lost to follow-up during the 8-week intervention period. Overall, this study demonstrates that a short, intensive, remotely delivered dietary intervention is a feasible approach to help manage adults with diabetes and early CKD who at high risk for CKD progression and cardiovascular complications.
Practical Application
The present study increases the knowledge of a telehealth approach to teach and monitor dietary interventions remotely to patients with early CKD and diabetes at risk for disease progression and cardiovascular complications. The healthcare community needs to embrace technology as an opportunity to reach patients and make efforts to improve the utility of these remote approaches. The current study provides preliminary data on the feasibility of this approach and informs future efforts to design larger lifestyle intervention trials.
Supplementary Material
Supplemental Item 1. Personalized Blood Pressure and Lab Report
Supplemental Item 2. Personalized Nutrition Report
Supplemental Table 1. Adherence with dietary data entry and dietitian telephone calls
Supplemental Table 2. Changes in 24-hour, Daytime, and Nighttime Ambulatory Blood Pressure and Albuminuria
Funding:
This work was supported by Geisinger Health Plan; the National Institutes of Health [NIDDK K23 DK 118198-01A1 to SJS, NIDDK K23 DK106515 to ARC]. The funders had no role in the study design, data collection, analysis and interpretation of data, in the writing of report and in the decision to submit the article for publication.
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Financial Disclosures: The authors have no relevant financial disclosures to report. The study was registered on clinicaltrials.gov (NCT03015480).
References
- 1.Kelly JT, Palmer SC, Wai SN, et al. Healthy Dietary Patterns and Risk of Mortality and ESRD in CKD: A Meta-Analysis of Cohort Studies. Clin J Am Soc Nephrol. 2017;12(2):272–9. Epub 2016/12/10. doi: 10.2215/cjn.06190616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Bach KE, Kelly JT, Palmer SC, et al. Healthy Dietary Patterns and Incidence of CKD: A Meta-Analysis of Cohort Studies. Clin J Am Soc Nephrol. 2019;14(10):1441–9. Epub 2019/09/26. doi: 10.2215/cjn.00530119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Chang A, Van Horn L, Jacobs DR Jr., et al. Lifestyle-related factors, obesity, and incident microalbuminuria: the CARDIA (Coronary Artery Risk Development in Young Adults) study. American Journal of Kidney Diseases : The Official Journal of the National Kidney Foundation. 2013;62(2):267–75. doi: 10.1053/j.ajkd.2013.02.363 [doi]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Lin J, Fung TT, Hu FB, Curhan GC. Association of dietary patterns with albuminuria and kidney function decline in older white women: a subgroup analysis from the Nurses’ Health Study. American Journal of Kidney Diseases : The Official Journal of the National Kidney Foundation. 2011;57(2):245–54. doi: 10.1053/j.ajkd.2010.09.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Gutierrez OM, Muntner P, Rizk DV, et al. Dietary patterns and risk of death and progression to ESRD in individuals with CKD: a cohort study. American Journal of Kidney Diseases : The Official Journal of the National Kidney Foundation. 2014;64(2):204–13. doi: 10.1053/j.ajkd.2014.02.013 [doi]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Goraya N, Simoni J, Jo CH, Wesson DE. Treatment of metabolic acidosis in patients with stage 3 chronic kidney disease with fruits and vegetables or oral bicarbonate reduces urine angiotensinogen and preserves glomerular filtration rate. Kidney international. 2014;86(5):1031–8. doi: 10.1038/ki.2014.83 [doi]. [DOI] [PubMed] [Google Scholar]
- 7.Estruch R, Ros E, Salas-Salvadó J, et al. Primary Prevention of Cardiovascular Disease with a Mediterranean Diet Supplemented with Extra-Virgin Olive Oil or Nuts. N Engl J Med. 2018;378(25):e34. Epub 2018/06/14. doi: 10.1056/NEJMoa1800389. [DOI] [PubMed] [Google Scholar]
- 8.The Look ARG. Effect of a long-term behavioural weight loss intervention on nephropathy in overweight or obese adults with type 2 diabetes: a secondary analysis of the Look AHEAD randomised clinical trial. The lancetDiabetes & endocrinology. 2014. doi: S2213–8587(14)70156–1 [pii]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hwang JH, Chin HJ, Kim S, et al. Effects of intensive low-salt diet education on albuminuria among nondiabetic patients with hypertension treated with olmesartan: a single-blinded randomized, controlled trial. Clinical journal of the American Society of Nephrology : CJASN. 2014;9(12):2059–69. doi: 10.2215/CJN.01310214 [doi]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Fang M Trends in Diabetes Management Among US Adults: 1999–2016. J Gen Intern Med. 2020;35(5):1427–34. Epub 2020/01/04. doi: 10.1007/s11606-019-05587-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Duncan I, Birkmeyer C, Coughlin S, et al. Assessing the value of diabetes education. Diabetes Educ. 2009;35(5):752–60. Epub 2009/09/29. doi: 10.1177/0145721709343609. [DOI] [PubMed] [Google Scholar]
- 12.Robbins JM, Thatcher GE, Webb DA, Valdmanis VG. Nutritionist visits, diabetes classes, and hospitalization rates and charges: the Urban Diabetes Study. Diabetes Care. 2008;31(4):655–60. Epub 2008/01/11. doi: 10.2337/dc07-1871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Slinin Y, Guo H, Gilbertson DT, et al. Prehemodialysis care by dietitians and first-year mortality after initiation of hemodialysis. Am J Kidney Dis. 2011;58(4):583–90. Epub 2011/07/02. doi: 10.1053/j.ajkd.2011.03.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kramer H, Jimenez EY, Brommage D, et al. Medical Nutrition Therapy for Patients with Non-Dialysis-Dependent Chronic Kidney Disease: Barriers and Solutions. J Acad Nutr Diet. 2018;118(10):1958–65. Epub 2018/08/05. doi: 10.1016/j.jand.2018.05.023. [DOI] [PubMed] [Google Scholar]
- 15.Verma S Early Impact Of CMS Expansion Of Medicare Telehealth During COVID-19 Health Affairs Blog2020 [cited 2020 September 30]. Available from: https://www.healthaffairs.org/do/10.1377/hblog20200715.454789/full/.
- 16.Lin J, Fung T, Hu F, Curhan G. Association of dietary patterns with albuminuria and kidney function decline in older white women: a subgroup analysis from the Nurses’ Health Study. Am J Kidney Dis. 2011;57(2):245–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lave J, Wenger E. Situated learning: Legitimate peripheral participation. New York: Cambridge University Press.; 1991. [Google Scholar]
- 18.Carver C, Scheier M. On the Self Regulation of Behavior. New York: Cambridge University Press; 1998. [Google Scholar]
- 19.Chang AR, Bailey-Davis L, Hetherington V, et al. Remote Dietary Counseling Using Smartphone Applications in Patients With Stages 1–3a Chronic Kidney Disease: A Mixed Methods Feasibility Study. J Ren Nutr. 2019. Epub 2019/05/13. doi: 10.1053/j.jrn.2019.03.080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Pan Y, Guo L, Jin H. Low-protein diet for diabetic nephropathy: a meta-analysis of randomized controlled trials. Am J Clin Nutr. 2008;88(3):660–6. [DOI] [PubMed] [Google Scholar]
- 21.Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. Journal of medical Internet research. 2006;8(4):e27. doi: v8i4e27 [pii]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Schwarzer R, Jerusalem M. Generalized Self-Efficacy scale. In: Weinman J, Wright S, Johnston M, editors. Measures in health psychology: A user’s portfolio Causal and control beliefs. Windsor, UK: NFER-NELSON; 1995. p. 35–7. [Google Scholar]
- 23.de Greeff A, Shennan AH. Validation of the Spacelabs 90227 OnTrak device according to the European and British Hypertension Societies as well as the American protocols. Blood Press Monit. 2020;25(2):110–4. Epub 2019/12/14. doi: 10.1097/mbp.0000000000000424. [DOI] [PubMed] [Google Scholar]
- 24.Conway J, Ingwersen L, Vinyard B, Moshfegh A. Effectivenss of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr. 2003;77(5):1171–8. [DOI] [PubMed] [Google Scholar]
- 25.Weiss B, Mays M, Martz W, et al. Quick Assessment of Literacy in Primary Care: The Newest Vital Sign. Ann Fam Med. 2005;3(6):514–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.U.S. Department of Agriculture ARS, Beltsville Human Nutrition Research Center, Food Surveys Research Group( Beltsville, MD) and U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics (Hyattsville, MD). What We Eat in America, NHANES 2015–2016 Data: Dietary Interview - Total Nutrients Intakes (2018, July). https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweianhanes-overview/ [accessed 07/13/18]. [Google Scholar]
- 27.Parmenter K, Wardle J. Development of a general nutrition knowledge questionnaire for adults. Eur J Clin Nutr. 1999;53(4):298–308. [DOI] [PubMed] [Google Scholar]
- 28.Kollipara U, Jaffer O, Amin A, et al. Relation of lack of knowledge about dietary sodium to hospital readmission in patients with heart failure. Am J Cardiol. 2008;102(9):1212–5. [DOI] [PubMed] [Google Scholar]
- 29.Prochaska J, Velicer W. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38–48. [DOI] [PubMed] [Google Scholar]
- 30.Guenther PM, Kirkpatrick SI, Reedy J, et al. The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 Dietary Guidelines for Americans. J Nutr. 2014;144(3):399–407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Maroni BJ, Steinman TI, Mitch WE. A method for estimating nitrogen intake of patients with chronic renal failure. Kidney international. 1985;27(1):58–65. [DOI] [PubMed] [Google Scholar]
- 32.Appel LJ, Champagne CM, Harsha DW, et al. Effects of comprehensive lifestyle modification on blood pressure control: main results of the PREMIER clinical trial. Jama. 2003;289(16):2083–93. Epub 2003/04/24. doi: 10.1001/jama.289.16.2083. [DOI] [PubMed] [Google Scholar]
- 33.He FJ, Li J, Macgregor GA. Effect of longer term modest salt reduction on blood pressure: Cochrane systematic review and meta-analysis of randomised trials. BMJ (Clinical research ed). 2013;346:f1325. doi: 10.1136/bmj.f1325. [DOI] [PubMed] [Google Scholar]
- 34.McMahon EJ, Bauer JD, Hawley CM, et al. A randomized trial of dietary sodium restriction in CKD. Journal of the American Society of Nephrology : JASN. 2013;24(12):2096–103. doi: 10.1681/ASN.2013030285 [doi]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Hwang JH, Chin HJ, Kim S, et al. Effects of intensive low-salt diet education on albuminuria among nondiabetic patients with hypertension treated with olmesartan: a single-blinded randomized, controlled trial. Clinical journal of the American Society of Nephrology : CJASN. 2014;9(12):2059–69. doi: 10.2215/CJN.01310214 [doi]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Filippini T, Naska A, Kasdagli M-I, et al. Potassium Intake and Blood pressure: A dose-response Meta-analysis of Randomized Controlled Trials. JAHA. 2020;9:e:015719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Liu Y, Tan R-S, Zhou D-Y, et al. The effects of protein intake on albuminuria in different estimated glomerular filtration rate: A population-based study. Eur J Intern Med. 2018;48:80–8. [DOI] [PubMed] [Google Scholar]
- 38.Rakova N, Juttner K, Dahlmann A, et al. Long-term space flight simulation reveals infradian thythmicity in human Na(+) balance. Cell Metab. 2013;17(1):125–31. [DOI] [PubMed] [Google Scholar]
- 39.Meuleman Y, Hoekstra T, Dekker F, et al. Sodium Restriction in Patients with CKD: A Randomized Controlled Trial of Self-Management Support. American Journal of Kidney Diseases. 2017;69:576–86. [DOI] [PubMed] [Google Scholar]
- 40.Humalda J, Klaassen G, de Vries H, van Dijk S, Navis G. A Self-management Approach for Dietary Sodium Restriction in Patients with CKD: A Randomized Controlled Trial. American Journal of Kidney Diseases. 2020;75(6):847–56. [DOI] [PubMed] [Google Scholar]
- 41.Titze J, Luft F. Speculations on salt and the genesis of arterial hypertension. Kidney int. 2017;91(6):1324–35. [DOI] [PubMed] [Google Scholar]
- 42.Scialla JJ, Appel LJ, Astor BC, et al. Net endogenous acid production is associated with a faster decline in GFR in African Americans. Kidney international. 2012;82(1):106–12. doi: 10.1038/ki.2012.82; 10.1038/ki.2012.82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Goraya N, Munoz-Maldonado Y, Simoni J, Wesson D. Treatment of Chronic Kidney Disease-Related Metabolic Acidosis With Fruits and Vegetables Compared to NaHCO3 Yields More and Better Overall Health Outcomes and at Comparable Five-Year Cost. . J Ren Nutr. 2020;31:239–47. [DOI] [PubMed] [Google Scholar]
- 44.Ikizler T, Burrowes J, Byham-Gray L, et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am J Kidney Dis. 2020;76(3):S1–S108. [DOI] [PubMed] [Google Scholar]
- 45.Ferrara A, Hedderson M, Brown S, et al. A telehealth lifestyle intervention to reduce excess gestational weight gain in pregnant women with overweight or obesity (GLOW): a randomised, parallel-group, controlled trial. Lancet Diabetes Endocrinol. 2020;8(6):490–500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Margolis K, Asche S, Bergdall A, et al. Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial. JAMA. 2013;310(1):46–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Bailey-Davis L, Wood G, Cook A, et al. Communicating personalized risk of diabetes and offering weight reduction program choice: Recruitment, participation, and outcomes. Patient Educ Couns. 2021;104(5):1193–9. [DOI] [PubMed] [Google Scholar]
- 48.Taylor R, Leslie W, Barnes A, et al. Clinical and metabolic features of the randomised controlled Diabetes Remission Clinical Trial (DiRECT) cohort. Diabetologia. 2018;61(3):589–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Becker S, Kribben A, Meister S, et al. User profiles of a smartphone application to support drug adherence--experiences from the iNephro project. PLoS One. 2013;8(10):e78547. Epub 2013/11/07. doi: 10.1371/journal.pone.0078547. [DOI] [PMC free article] [PubMed] [Google Scholar]; A. Kribben has received funding from Roche Pharma for consultancy. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
- 50.Boulos MN, Wheeler S, Tavares C, Jones R. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online. 2011;10:24. Epub 2011/04/07. doi: 10.1186/1475-925x-10-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Item 1. Personalized Blood Pressure and Lab Report
Supplemental Item 2. Personalized Nutrition Report
Supplemental Table 1. Adherence with dietary data entry and dietitian telephone calls
Supplemental Table 2. Changes in 24-hour, Daytime, and Nighttime Ambulatory Blood Pressure and Albuminuria